Data Lake
In the past years, the concept of data lake looked like much needed in the enterprises. But what does data lake mean? How does it benefit organizations? What are the advantages of Data Lake by incorporating into the architecture? Are organizations adapting data lake model?
What is a Data Lake?
First, let us define what data lake actually means.
“A data lake is a storage repository that contains massive amount of raw data in native formats, to enable users to easily access to large volumes of structured (rows and columns), semi-structured (CSV, logs, XML, JSON), and unstructured (emails, documents, PDFs) data as needed.”
The main idea of Data Lake is to have a centralized store of all data in enterprises ranging from raw data (primary data) to transform the data when required into various tasks which include reporting, visualization, analytics and machine learning.
Next-Generation Data – Modernizing Enterprise Hub
Data Lake architecture has become a scalable data storage operation for many businesses and it plays a crucial to the future of business that can be flexible for all types of data, process, and query data in many ways.
Understanding the Data Lake Architecture with Hadoop
The data lake is a single repository capable of storing huge volumes of data in various formats. Big organizations like Facebook, Google, Yahoo and other web scale companies have gained numerous benefits and advanced to the next level by using data lakes.
The successful Hadoop journey typically begins with new analytic applications, which lead to a Data Lake. The new applications which are created and derive value from the new formats of data from webserver logs, databases, social media, clickstreams, and other sources, the Data Lake is formed with Hadoop acting as a shared service to create a diverse set of value chain for efficient scale which new types of business values are emerged. In addition to this, curation takes through capturing, mixing, and exploring new types of data making it in available in the data catalog .
The Best Describe Advantages of Data Lake
A data lake is used to end the data silos to centralize the data and to gain flexible access to all diverse data sources within your business.
- Low Cost and Extremely Scalable for Processing: The cost of data lake is low and it enhances scalability to extreme high volumes of data processing for high efficiency.
- Compatibility with Multiple Platforms: The raw data that is stored in the data lake has capabilities where developers can work effectively in multiple programming languages like Java or Python and frameworks technologies such as Pig and Hive.
- Data Accessibility: A data lake can contain any data such as structured, semi-structured and unstructured in a single centralized location. This not only enables users to allow but also grant immediate access to all data, since it is in one location.
- Data do not have to be moved: Since all the data are stored in one central location, silos are no longer necessary and it gives easy accessibility. Additionally, it is not necessary to move data from one warehouse to another.
- Insights of Data Lake: Organizations can store the data in raw format with Data Lake. This means that the information is stored in a secure location and does not fear the loss of data.
The Data Lake can be effective data management solutions for advanced analytics solutions and user-interface. However, there are some security challenges that have come across when it comes to storing in one particular location. The objective of building a data lake is to derive value and if it is done correctly and stored in single repository, the data can be easily accessible and analyze the raw data with existing data analytics tools, which gives significant new insights.
Data Lake and Enterprise Data Warehouse together provide synergy of capabilities to allow users to do more with data and actually drives incredible business results faster.
